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The provision of affordable housing is a pressing challenge that exists around the globe. Successful completion of affordable housing projects is, therefore, of great significance in both developed and developing countries. The primary objective of this paper is to identify the Critical Success Factors (CSFs) for affordable housing projects. To achieve this a comprehensive review of international literature is conducted to identify relevant factors. Nine groups of CSFs were formed, namely: Policy and government support; Land and planning process; Role of Financial Institutions and funding aspects; Sustainability; Designing and materials selection; Approvals, procedures, and clearances; Project management and value engineering; Infrastructure development of Project; and Facility Management. Subsequently, a survey is conducted with experts in India in order to validate and rank the criticality of the identified success factors in the Indian context. Expert rankings are provided for a range of affordable housing products. The results indicate that ‘policy and government support’, ‘land and planning process’, ‘role of financial institutions and funding aspects’ and ‘approvals, procedures and clearances’ are generally the top four CSFs for affordable housing projects. However, the results also indicate that importance of the CSFs was found to vary across different housing products (social, public, and private housing and rental and ownership models). A secondary objective of this paper is to identify differences in approaches to the implementation of the established CSFs in practice. The paper presents a case study comparison between India (a lower-middle income country) and the UK (a high income developed country). Pune in India and London in the UK are compared to determine how well countries of different income levels are achieving the established CSFs. Results suggests a variation in the local contexts in the delivery of successful affordable housing and it is found that developed countries are better at government support and policies, land planning, and incentives to developers for affordable housing delivery.

Pine needle is a typical biomass which is abundantly available in Uttarakhand hills. This shredded biomass contributes significantly in forest fire occurring regularly in Uttarakhand. Different energy harnessing routes as direct combustion, anaerobic digestion, pyrolysis, gasification, and briquetting for pine needles were reviewed. These routes were further compared on the basis of energy consumption and energy efficiency of the process as per the available literature. The review suggested that briquetting of pine needles and its anaerobic digestion are two most energy efficient methods having energy efficiency of 88% and 41.6% respectively. The estimated energy required for briquetting of 1 ton pine needle was 1370.5 MJ whereas for gasification it was 1170 MJ.

In India and many developing countries harvesting of onion (Allium cepa L.) crop is done manually which is cumbersome and time-consuming. The tractor-operated front-mounted twin conveyor onion digger was designed and developed during 2019 in Divisional Workshop (Farm Machinery and Power Engineering) at Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand. It had five blades made of mild steel (320 mm × 150 mm × 12 mm size) bolted to the base plate instead of one single share to strengthen the share. Onion and soil were separated over conveying unit. Power from the rear PTO (Power take-off) of a tractor to the front-mounted digging unit was conveyed through a power transmission unit which consisted of a gear reduction unit, V-belt and pulley arrangement, and 40 mm diameter of the propeller shaft. The lifting, lowering, and depth were controlled with the help of two single-acting hydraulic cylinders. The biometric and physical properties of onion bulbs (Agri found light red) were found average with equatorial and polar diameter, plant height, neck thickness, and weight of onion dry leaves as 36.19 and 37.19 mm, 284.05 mm, 6.80 mm and 30.19 g respectively. The average soil cone index, moisture content, and bulk density (before transplanting and harvesting) of soil were observed to be 1 kg/cm2, 17.22,12.58%, and 1.50, 1.38 g/cm3 respectively. The performance evaluation in the field found a maximum percentage of exposed and minimum damaged bulbs as 82.55% and 13.51% respectively, at a forwarding speed of 1.5 kmph and conveyor speed of 2 kmph. The field efficiency of the machine was 87.46%.

This book chapter primarily discusses various applications and historical developments of artificial intelligence (AI) and machine learning (ML) applications in financial sectors, specially from the perspective of Indian capital markets. AI and ML are unique technologies that may be used in a variety of sectors. Especially for financial sectors, these bring a lot of advantages, such as Lesser human bias; quicker resolution of issues; cheaper than human labor; greater reach and impact; increasing client involvement; and reducing fraudulent activity. It also talks about various challenges, and charts the future outlook of AI and ML applications. AI and ML will persist to have a growing influence on the finance industry in the upcoming times.

Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%.

Urbanization plays a crucial role in the urban landscape dynamics, contributing to numerous ecosystems and urban climate changes. Diurnal variation of land surface temperature (LST) is a significant index for estimating local climate change as a response to urbanization. This research analyzes the impact of urbanization on the LST-based diurnal temperature range (DTR) for Pune, India, in three steps: (a) detection of spatiotemporal variation in DTR, (b) assessment of DTR behavior in different land use and land cover (LULC) classes, and (C) examining the interrelationship between urban density and DTR. The study utilizes a time series LST estimates from the MODIS satellite for 12 years (2003–2014). The preliminary spatiotemporal assessment shows a decrease in annual averaged DTR across Pune, from 25.79 °C in 2003 to 21.82 °C in 2014. Further investigation in LULC classes revealed a similar downward non-monotonic DTR trend in all classes except for the Built-up class, exhibiting a significant monotonous downtrend with a decrease of 5.67 °C, and the DTR anomalies are also consistent with this trend. The Mann–Kendall test confirms a significant trend with a p-value of 0.029, and Sen's slope analysis with − 0.167 establishes a decreasing DTR trend with a negative slope. The evaluation of urban density in Pune Metropolitan Region (PMR) for DTR variation shows a rise in area under DTR (below 17 °C) in the Dense Built-up class from 0.1 to 7.52% across 2003–2014. Whereas DTR (above 25 °C) in Less Dense Built-up saw a sharp decrease in the area from 16.37 to 0.05% during the same period. Thus the DTR trend in varying urban densities signifies the role of intense urbanization on DTR behavior. These findings are alarming and provide insight into the local climate issues that should help policymakers and urban planners to make informed decisions toward sustainable development in the Pune Metropolitan Region.

Smegmoliths are hard local accumulations of smegma, mostly seen in children with phimosis. This is due to difficulty in adequate cleansing of the subpreputial area. It is not a common finding in adults. We report a case of smegmolith occurring in an adult male as a manifestation of dermatitis neglecta. Dermatitis neglecta occurs secondary to improper hygiene when a person avoids cleansing a part of his body either due to physical disability, neurodeficits or psychiatric disorder. It should be diagnosed promptly as only cleansing the part with nwormal saline would effectively treat this condition without the need for any sophisticated investigation and intervention. In the index case, smegmolith has occurred because of dermatitis neglecta in an otherwise healthy male without any underlying comorbidity.

SARS-CoV-2, the causing agent of coronavirus disease (COVID-19), first broke out in Wuhan and rapidly spread worldwide, resulting in a global health emergency. The lack of specific drugs against the coronavirus has made its spread challenging to control. The main protease (M

In finance, a portfolio is a person's or company's total financial holdings. Portfolio risk and expected return are managed via portfolio optimization. Portfolio optimization is a kind of diversification that decreases portfolio risk by combining assets with varying risk profiles. Since the global financial crisis of 2008, asset management practices have undergone a sea change. This study examines a wide range of artificial intelligence (AI)-based asset management systems, focusing on the most urgent concerns and highlighting the benefits in the analysis of fundamentals and producing new investment strategies. Trading is another area where AI is making a big impact. One of the most intriguing aspects of AI is its ability to analyze vast amounts of data and generate trading tips. Using AI in asset management comes with certain disadvantages as well. AI models are difficult for managers to keep track of since they are often complex and opaque. This research provides a throughout overview of the avenues where AI is used in financial portfolio management.

Black point of wheat is a commonly occurring disease in all wheat growing regions of the world. It is characterized by the dark discoloration of the embryo side of the seed. Yellowing of leaves in early season and foliar blights on mature crop are common symptoms causing losses up to 20%. Therefore, present study was carried out on seeds of ten popularly cultivated wheat varieties of north India viz. HD 2967, HD 3086, PBW 502, PBW550, PBW 677, PBW 723, PBW725, WH 711, WH 1105 and DBW 17. Maximum incidence of black point was observed on seeds of var. HD 3086 and DBW 17 while it was minimum on var. PBW 502. The most prevalent fungal pathogens associated with diseased seeds are Alternaria triticina, Bipolaris sorokiniana and Fusarium graminearum. Association of black point in seeds reduced the germination and vigour index of all the varieties under study. Maximum decrease in seed germination was recorded in var. HD2967 followed by DBW 17. Pathogens harboring black point affected seeds induced seed rot and seedling mortality thus reducing seed germination and ultimately poor crop stand under field conditions.

More than 90% of people make their most expensive purchase when they buy a residential property. This expensive purchase takes much time to decide what, where, when, why and how to buy. Many factors are involved to answer all the questions to make the accurate decision. These factors impact the decision-making capacity of the buyers. Factors like demographics, physiographic, motivational, aspirational, tendency of herd behavior all impacts the decision-making capacity. In comparison to common purchases which involves less effort like our daily routine purchase takes less amount of time and money to make the accurate decision, Residential property buying requires high involvement in terms of time, effort and money with lot of factors analysis which can effects the buying decision of prospective buyer. This research paper aims to analyze all those factors which are going to impact the decision making capacity of residential property buyer, especially in post Covid 19 situation, what all factors are going to become crucial for buyer while buying property? and what relevance they hold in respect of each other? The paper aims to help both developer and buyer to tick all those boxes which are going to satisfy requirements of both. Also, while considering these factors it will be easy for developer to channelize sales and prospective buyers will get what they really want.

The aromatic genus Ocimum L. is commercially and medicinally important in the family Lamiaceae was known by 180 genera and 3500 species spread from tropical to sub-tropical parts of the world, of which O. basilicum L. prefers both plains and high altitudes. The species is the most important of all as its various subspecies, varieties and chemotypes contain a number of terpenoids and phenols along with many other compounds of high medicinal and commercial values. It is a natural tetraploid (2n = 4x = 48 chromosomes) with almost normal mitosis and meiosis and belongs to the sub-genus Basilicum with basic chromosome number x =12. Free intervarietal hybridization in the species has resulted in a number of viable and stable chemotypes with methyl chavicol, linalool, eugenol, camphor and methyl cinnamate as main constituents of their essential oils, either occurring singly or in different combinations. This feature of the chemotypes has been suggested to be genic in nature as an alteration in their chromosome number and structure is a rare phenomenon. While α-phenylalanine has been found to be the precursor of terpenoids and phenols boch, monoterpenoid linalool has been considered to be the initial terpenoid in the biogenesis of the two compounds. A semblance in the biogenetic pathways of the said main compounds in the various species of the genus Ocimum, including the one understudy, with those of Mentha L, looks plausible, which throws light on homogeneity in the family Lamiaceae. Various aspects of the species, such as distribution, cytology, genetics, biogenesis, biotechnology, etc. of the commercially important species have been discussed in detail.

Drought is a multidisciplinary concept that can have major impacts on ecological, agricultural, and socio-economic spheres. The accurate monitoring and information of spatio-temporal distribution of agricultural drought are effective means of reducing the farmers’ losses. The space-based technology is proven to provide the detail of land information, which helps to quantify different natural resources and even severity of climate extremes. The biophysical parameters such as precipitation, temperature, soil, and vegetation significantly influence drought monitoring in the regions. An artificial Neural Network (ANN) model based on the integration of Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Soil Condition Index (SCI) derived from multi-source remote sensing satellite imagery was applied for assessment and prediction of drought. The study was implemented in Sangli, Maharashtra, India from the period 2003–2016 to assess the efficiency of the ANN model for near real-time drought monitoring. In our research, the investigation results for 2006, 2011, and 2016 (a typical dry year) were accurately explored using the ANN model. The multiyear ANN model results were also checked with the actual drought intensity of government records and observation. The proposed system demonstrated 92% accuracy and matched the trend with the ground base stations, hence the ANN model demonstrates the potential of using as drought monitoring indicator capturing both meteorological and agricultural drought information. The output of the study can be utilized for contingency planning by state and local governments.

Eleven culture parameters were screened by the Plackett–Burman design for pyruvate production to develop a standard protocol for media formulation. In an incubator shaker, the screened optimal conditions for pyruvate production were: carbon source 24.642g/L, nitrogen source 11.89 g/L, KH2PO4 1.185 g/L, thiamine 13.302 µg/L, biotin 13.927 µg/L, CaCO3 43.432g/L, MgSO4•7H2O 0.346 g/l, inoculum age 3 days, inoculum volume 6% (v/v), agitation 220 rpm, incubation time 46.412 h and incubation temperature 34.582℃. Except CaCO3 all factors were significant and agitation was considered a dummy factor in the experiment.

SARS-CoV-2, the causing agent of coronavirus disease (COVID-19), first broke out in Wuhan and rapidly spread worldwide, resulting in a global health emergency. The lack of specific drugs against the coronavirus has made its spread challenging to control. The main protease (Mpro) is a key enzyme of SARS-CoV-2 used as a key target in drug discovery against the coronavirus. Medicines derived from plant phytoconstituents have been widely exploited to treat various diseases. The present study has evaluated the potential of Illicium verum (star anise) phytoconstituents against Mpro by implementing a computational approach. We performed molecular docking and molecular dynamics simulation study with a set of 60 compounds to identify their potential to inhibit the main protease (Mpro) of SARS-CoV-2. DFT study and post dynamics free energy calculations were also performed to strengthen the findings. The identified four compounds by docking study exhibited the highest potential compared to other selected phytoconstituents. Further, density functional theory (DFT) calculation, molecular dynamics simulation and post dynamics MM-GBSA energy calculation predicted Verimol-G as a potential compound, which formed stable interactions through the catalytic dyad residues. The HOMO orbital energy (-0.250038) from DFT and the post dynamics binding free energy calculation (-73.33 Kcal/mol) correlate, suggesting Verimol-G is the best inhibitor compared to the other phytoconstituents. This compound also complies with the ADME properties of drug likeliness. Thus, based on a computational study, we suggest that Verimol G may be developed as a potential inhibitor against the main protease to combat COVID-19. Communicated by Ramaswamy H. Sarma

Introduction: Dermatophytosis is a fungal infection of the skin, hair, and nails. In the past several years, it has emerged as a general public health problem in our country. Studies from different regions reveal varying patterns of etiological distribution of the disease. Aims and Objectives: To estimate the prevalence of different fungal species associated with dermatophytosis and to find out any possible association of the type of fungus with different clinical parameters of the disease. Materials and Methods: This was a cross-sectional study among 311 clinically diagnosed dermatophytosis cases from a tertiary care center in eastern India. Potassium hydroxide (KOH) mount and fungal culture were done from samples of skin, hair, and nails, and various clinical parameters were analyzed. Results: There was a male preponderance among cases and maximum patients belonged to third decade of life. Most common presentation was tinea corporis et cruris (39.5%). Family history was positive in 48.8% of cases. Trichophyton mentagrophytes was the most common fungal species (79.91%) grown in culture followed by Trichophyton rubrum (13.53%). Majority of patients had a mild body surface area involvement. We did not find statistically significant association of any clinical parameters with type of organism isolated. Conclusion: Trichophyton mentagrophytes was the most common isolated fungal species. This is in contrast to several studies where T.rubrum was the frequently found organism. There was no significant association of any clinical parameters like body surface area, number of sites, or duration of diseasewith fungal species isolated in culture.